Harvesting model uncertainty for the simulation of interannual variability

Abstract

[1] An innovative modeling strategy is introduced to account for uncertainty in the convective parameterization (CP) scheme of a coupled ocean-atmosphere model. The methodology involves calling the CP scheme several times at every given time step of the model integration to pick the most probable convective state. Each call of the CP scheme is unique in that one of its critical parameter values (which is unobserved but required by the scheme) is chosen randomly over a given range. This methodology is tested with the relaxed Arakawa-Schubert CP scheme in the Center for Ocean-Land-Atmosphere Studies (COLA) coupled general circulation model (CGCM). Relative to the control COLA CGCM, this methodology shows improvement in the El Niño-Southern Oscillation simulation and the Indian summer monsoon precipitation variability. Copyright 2009 by the American Geophysical Union.